Searching for “hits", the molecules with desired therapeutic potentials, is a critical task in drug discovery.
Instead of screening a library of countless potential candidates in a brute-force manner, designing drugs with sample-efficient generative models has been highlighted as a promising strategy.
While many generative models for drug design are trained on the distribution of known active compounds [1]}, [2]}, [3]},
such models tend to produce molecules that are similar to that of the training dataset [4]}, which discourages finding novel molecules.
<FIGURE>